Spitz Nevi and Spitzoid Melanomas: Exome Sequencing And

Modern Pathology (2017) 30, 640–649

Spitz nevi and Spitzoid melanomas: exome sequencing and comparison with conventional melanocytic nevi and melanomas Rossitza Lazova1,2,5, Natapol Pornputtapong2,5, Ruth Halaban1, Marcus Bosenberg1,2, Yalai Bai2, Hao Chai3 and Michael Krauthammer2,4

1Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA; 2Department of Pathology, Yale University School of Medicine, New Haven, CT, USA; 3School of Public Health, Yale University School of Medicine, New Haven, CT, USA and 4Program in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, CT, USA

We performed exome sequencing of 77 melanocytic specimens composed of Spitz nevi (n = 29), Spitzoid melanomas (n = 27), and benign melanocytic nevi (n = 21), and compared the results with published melanoma sequencing data. Our study highlights the prominent similarity between Spitzoid and conventional melanomas with similar copy number changes and high and equal numbers of ultraviolet-induced coding mutations affecting similar driver genes. Mutations in MEN1, PRKAR1A, and DNMT3A in Spitzoid melanomas may indicate involvement of the protein kinase A pathway, or a role of DNA methylation in the disease. Other than activating HRAS variants, there were few additional mutations in Spitz nevi, and few copy number changes other than 11p amplification and chromosome 9 deletions. Similarly, there were no large-scale copy number alterations and few somatic alterations other than activating BRAF or NRAS mutations in conventional nevi. A presumed melanoma driver mutation (IDH1Arg132Cys) was revealed in one of the benign nevi. In conclusion, our exome data show significantly lower somatic mutation burden in both Spitz and conventional nevi compared with their malignant counterparts, and high genetic similarity between Spitzoid and conventional melanoma. Modern Pathology (2017) 30, 640–649; doi:10.1038/modpathol.2016.237; published online 10 February 2017

Recent exome sequencing studies have successfully cancer-related genes in 30 Spitz nevi,10 a type uncovered several novel melanoma driver genes, of benign nevus with characteristic 11p amplifica- including PPP6C, RAC1, and ARID2,1–6 allowing for tions, concurrent HRAS mutations, and distinct a better understanding of the pathways dysregulated histopathological features. However, we are not in the disease. The results of these studies were also aware of a large-scale, comprehensive, and unbiased instrumental in classifying melanoma into four genomic sequencing effort interrogating these major subgroups, based on the mutational status of lesions. BRAF, RAS, and NF1.3,7,8 To address this, we exome-sequenced 29 Spitz Although much has been learned about the genetic nevi, 27 Spitzoid melanomas, and 21 conventional causes of melanoma, the molecular changes under- melanocytic nevi to compare two types of melano- lying benign melanocytic lesions are known to a cytic neoplasm: (1) Spitz nevi to Spitzoid melanomas, because they share common histopathological lesser degree. For example, small sequencing features such as epithelioid or spindled melanocytes, studies were performed on specific nevi subtypes, 9 and (2) conventional melanocytic nevi to conven- including congenital nevi. In addition, a recent tional melanoma (non-Spitzoid, referred to as mela- study interrogated the mutational status of 182 noma below), using data from a recent exome screen in melanoma.7 We hypothesized that a comprehen- Correspondence: M Krauthammer, MD, PhD, Department of sive exome-wide sequencing effort of these lesions Pathology, Yale University School of Medicine, 300 George Street, should facilitate (a) a better understanding of the New Haven, CT 06511, USA. nevi mutational landscape, (b) insights into early E-mail: [email protected] 5These authors contributed equally to this work. mutational changes, and (c) a refined assessment of Received 6 May 2016; revised 12 December 2016; accepted 12 published melanoma driver genes by studying their December 2016; published online 10 February 2017 presence or absence in nevi. Spitzoid lesions were of

www.modernpathology.org Spitz nevi and Spitzoid melanomas R Lazova et al 641 particular interest because there is an urgent need to discover markers that can accurately diagnose benign Spitz nevi present predominantly in children and young adults, and differentiate them from Spitzoid melanomas. The results of the studies can also address the question whether Spitz nevi and Spitzoid melanomas represent early and late stages of a distinct melanocytic subgroup with low UV exposure, as reported before.11

Materials and methods Collection of Samples and Clinical Data The 29 Spitz nevi and 27 Spitzoid melanomas were retrieved from the Yale Spitzoid Neoplasm Reposi- tory. All cases were reviewed by six experienced dermatopathologists and only cases with an unequi- vocal consensus diagnosis were included in the study. Spitz nevi were defined as benign melanocytic nevi composed of large epithelioid, oval, or spindled melanocytes arranged in nests and/or fascicles (Figures 1a–c). Spitzoid melanoma was defined as a melanoma, histopathologically resem- bling a Spitz nevus and composed of large epithelioid melanocytes. Histopathological criteria used in the evaluation and classification of a lesion as a Spitzoid melanoma included (but were not limited to) the following: asymmetry, sheet-like growth pattern, expansile nodular growth, lack of matura- tion, ulceration of the epidermis, presence of mitotic figures, deep or atypical mitotic figures, deep extension into the subcutis, or large size (41 cm) (Figures 1d–f). The following characteristics were recorded for Spitzoid melanomas: tumor thickness, primary anatomic location, age, gender, presence or absence of ulceration, and mitotic rate in accordance Figure 1 (a) A Spitz nevus (G728T) presenting as a symmetric, wedge-shaped and well-circumscribed melanocytic proliferation. with the American Joint Committee on Cancer (b) Vertically oriented nests of melanocytes with clefts between (AJCC). In addition, 21 conventional melanocytic them and the surrounding hyperplastic epidermis. (c) The nevi were also included. Clinical and histopatholo- melanocytes are monomorphous and mitotic figures are absent. gical data were recorded for all cases. Two 1-mm (d) A Spitzoid melanoma (G762T) showing asymmetric growth cores were obtained from each sample for DNA pattern. (e) The melanocytic nests vary markedly in size and shape and focally form sheets. The arrow marks a narrow ulceration of extraction. Adjacent normal tissue was used as a the epidermis. (f) The melanocytes are pleomorphic and display control. Data for the conventional melanoma sam- prominent nucleoli. The two arrows point to mitotic figures in the ples were reused from a prior study.7 dermis.

Exome Sequencing and Variant Calling with a mean coverage of 125 ± 26 independent reads Exome sequencing and somatic variant calling was per base, mean error rate of 0.36%, and with 93% of performed as described.3,7 We used the NimbleGen the targeted bases covered with at least 20 indepen- SeqCap EZ Exome v.2 Capture Kit, and performed dent reads. Owing to the slightly higher error rate paired-end sequencing on Illumina HiSeq. We used compared with our earlier sequencing effort in bwa and SAMtools for read alignment and single- conventional melanoma, we manually reviewed nucleotide variant calling, using fully validated and filtered the top recurrent somatic hits in our filtering criteria for identifying high-quality DNA samples. variants.3,7 Tumor DNA sequencing was performed with a mean coverage of 231 ± 56 independent reads Sanger Validation per base, mean error rate of 0.44%, and with 96% of the targeted bases covered with at least 20 indepen- The mutations in PRKAR1A and IDH1 were vali- dent reads. Normal DNA samples were sequenced dated by Sanger sequencing of the DNA extracted

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from the formalin-fixed and paraffin-embedded the first stage. This stage produced a final tree tumors using forward 5′-GAGTGCCAGCTTTACA with optimal features selected, and optimal cut- TGCC-3′ and reverse: 5′-CCGCATCTTCCTCCGTGT points for those features. We used all available AG-3′ primers that amplified 211 bp in PRKAR1A, patients for comparing Spitz nevi to Spitzoid and 5'- GGCACGGTCTTCAGAGAAGCC-3' and 5'-TG melanomas. The algorithm in the Scikit-learn pack- CCAACATGACTTACTTGATCCC-3' primers that age performed weighted sampling to balance the amplified 118 bp in IDH1, each surrounding the number of samples in comparing conventional nevi mutation site. to melanoma.

Copy Number Variant Calling MC1R Single-Nucleotide Variant Status Sequenza12 was used to derive allele-specific copy MC1R germline variants were classified as functional ‘ ’ ‘ ’ number, ploidy, and tumor purity from our data. We R or non-function r alleles based on published 15–17 developed a script to extract arm-specific losses and data or, for novel variants, based on in silico ‘ ’ gains from the Sequenza segmentation data. The predictions by PolyPhen and Sift. Unlike r alleles, R normalized fold depth ratio was visualized using variants are thought to be damaging to protein Circos.13 function and/or known to be associated with red hair and light skin phenotypes. The members of each allele group are shown in Supplementary Table 2. Survival Analysis We assessed genetic changes for their effect on 20/20 Analysis survival, using the univariate Cox proportional We classified the top mutated genes based on the hazard model. Three common statistical tests were 20/20 heuristic rule.7,18 The rule selects genes in performed, namely likelihood ratio test, Wald test, which 20% or more of the observed somatic and score test. Common gene mutations and chro- mutations are at recurrent sites, or in which 20% mosomal aberrations were represented as Boolean or more of the observed somatic mutations are values, that is, true and false, and measured across inactivating (ie, nonsense mutations, splice-site the following known melanoma driver genes and mutations, or InDels). Additionally, we used the mutations: BRAF (position 600), NRAS (positions Fisher’s exact test to compare the number of samples 12,13, and 61), and any predicted damaging muta- with somatic mutations in Spitzoid and conventional tions in NF1, TP53, CDKN2A, ARID2, PTEN, RAC1, melanoma. CYP7B1, and PPP6C. Chromosomal aberrations were recorded for each chromosome arm separately. Results and discussion Machine Learning and Sample Classification Sample Cohort Based on similar features as discussed above, a The Spitz nevi cohort consisted of 29 patients, 15 decision-tree learning approach was used to classify females and 14 males (F:M = 1.1:1) that ranged in age nevi and melanomas based on exome sequencing between 1 to 35 years old (mean 8.8; median 7) (see data. Additional features included the total number Supplementary Table 1) . The lesions were distrib- of somatic mutations and chromosomal aberrations uted, in decreasing order, on the lower extremity (12) assessed for common alternations found in mela- head and neck (7) upper extremity (7) and trunk (3). noma, that is, 6q, 8p, 9p, and 10q losses, as well as, The follow-up ranged between 5 and 25 years with a 1q, 6p, 7p, 7q, 8q, 17q, and 20q gains. A decision-tree mean of 14.6 years and a median of 14 years. All classifier14 in the Scikit-learn package performed the patients were alive and with no evidence of decision-tree learning. The package allows the user recurrence or metastasis at the conclusion of the to input tree complexity, that is, the number of study. The Spitzoid melanoma cohort consistent of decision node levels, and performs feature selection patients 34 to 89 years old (mean 64; median 65). given a set of input features. The classifier was There was no gender predominance. The lesions trained in two stages. In the first stage, we performed were distributed as follows in a decreasing order: decision-tree learning using a leave-one-out cross- trunk (8) lower extremity (6) upper extremity (7) and validation. We then increased tree complexity by head and neck (6). Tumor thickness ranged between one for each run. This stage provided the optimal 1 and 9 mm, with a mean of 3.7 mm and a median of tree complexity for performing the classification, as 3.1 mm. The follow-up ranged between 0.5 and 15.5 well as the mean F1 score measured across the folds years, with a mean of 6.6 years and a median of (we use the term accuracy for this measure across the 7 years. Seventeen patients were alive with no remainder of the manuscript). In a second stage, we evidence of recurrence or metastasis, 6 were dead ran decision-tree learning over the whole data set, of disease, 1 expired of unknown cause, 2 patients setting the tree complexity to the one determined in were alive with disease, and 1 patient was lost to

Modern Pathology (2017) 30, 640–649 Spitz nevi and Spitzoid melanomas R Lazova et al 643 follow-up at the conclusion of the study. The 21 mutations was significantly different between nevi conventional nevi came from patients between and melanoma (both of the Spitzoid and conven- 3 months and 14 years of age (mean age = 8.5 and tional type), with 9 ± 3 and 758 ± 97 mutations in median = 11 years); 10 males and 11 females. In nevi and conventional melanomas (P = 2.35E − 12), decreasing order, the location of the nevi was: head respectively, and 34 ± 9 and 747 ± 138 mutations in and neck (14) trunk (6) and lower extremity (1). The Spitz nevi and Spitzoid melanomas (P = 2.09E − 05), follow-up ranged between 3 and 12 years, with a respectively. On the other hand, there was no mean of 9.9 and median of 11 years. The 133 statistical difference in the mutational counts conventional melanoma samples, all from sun- between the two types of nevi, and the two types of exposed skin, have been described earlier.7 They melanoma. All subtypes showed a characteristic UV consisted of 105 metastatic, and 28 primary samples. mutation spectrum with ~ 90% C4T mutations in The majority of the latter were nodular melanomas the dipyrimidine context (Figure 3). (12) followed by superficial spreading melanoma (6) There was a large overlap and commonalities in desmoplastic melanoma (1) spindle cell melanoma driver genes in Spitzoid melanomas and melanoma.7 (1) lentigo maligna melanoma (1) and 7 melanomas Particularly, mutations affecting the MAPK pathway of unknown type. Tumor thickness ranged between were present in 66% of the lesions, with 37% (10/27) 0.2 and 22 mm, with a mean of 3.9 mm and a median BRAF-mutant, 18.5% (5/27) RAS-mutant, and 11.1% of 2.6 mm for the full cohort, and between 0.55 and (3/27) NF1-mutant Spitzoid melanomas. NF1-mutant 22 mm, with a mean of 3.9 mm and a median of Spitzoid melanomas harbored additional RASopathy 3.3 mm for the primary samples only. gene mutations, as recently reported in conventional NF1-mutant melanomas.7 Of the three NF1-mutant Spitzoid melanomas, one sample harbored two Mutational Landscape RASA2 (Pro530Leu, Gln500Ter), one a PTPN11 (Thr73Ile), and another a MAP2K1 (Pro124Leu) The number of somatic silent and non-silent single- mutation (Supplementary Table 3). Spitzoid mela- nucleotide variants, splice-site variants, and InDels nomas featured additional hotspot or inactivating differed markedly among the samples (Figure 2, mutations typically found in melanoma, including Table 1, and Supplementary Table 1). The number of changes in CDKN2A, TP53, RAC1, PTEN, IDH1, and

Figure 2 Mutational landscape of Spitz nevi, Spitzoid melanomas compared with conventional nevi and melanomas. The 133 conventional melanomas correspond to paired samples from a previous publication.7

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Table 1 Exome-sequenced cohort

Nevus Spitz nevus Spitzoid melanoma Conventional melanoma

Number of samples 21 29 27 133

Sex Female 11 17 13 46 Male 10 12 14 87

Number of somatic mutations Mean with standard error 9.3 ± 3.1 34.4 ± 9.4 747.2 ± 137.6 758.0 ± 96.8

Age (year) Mean with standard error 8.3 ± 0.6 8.8 ± 1.3 64.1 ± 2.8 67.2 ± 1.2 Min 0.3 4 34 37 Max 14 25 89 94

The 133 conventional melanoma samples correspond to paired samples from a previous publication.7

Figure 3 Mutation spectrum. Observed mutations in non-dipyrimidines (left) and dipyrimidines (right).

Table 2 Somatic mutations at recurrent positions in Spitzoid melanomas

No. of % Recurrent No. of % Recurrent Gene Gene ID samples mutations Gene Gene ID samples mutations

BRAF ENSG00000157764 10 66.7 MAP2 ENSG00000078018 2 66.7 NRAS ENSG00000213281 4 100 RAB31 ENSG00000168461 2 66.7 PCNXL2 ENSG00000135749 4 30.8 ZSCAN2 ENSG00000176371 2 66.7 STARD13 ENSG00000133121 3 42.9 ABCC3 ENSG00000108846 2 50 TBC1D9 ENSG00000109436 3 37.5 LRRC1 ENSG00000137269 2 50 BNIP2 ENSG00000140299 2 100 LRRC16A ENSG00000079691 2 50 GORAB ENSG00000120370 2 100 NCOR1 ENSG00000141027 2 50 IDH1 ENSG00000138413 2 100 AXL ENSG00000167601 2 40 KLF7 ENSG00000118263 2 100 DLG5 ENSG00000151208 2 40 MMP2 ENSG00000087245 2 100 ADAM23 ENSG00000114948 2 33.3 MRPL1 ENSG00000169288 2 100 GRIP1 ENSG00000155974 2 33.3 NAA15 ENSG00000164134 2 100 PCDHB2 ENSG00000112852 2 33.3 SLC15A2 ENSG00000163406 2 100 PLCL2 ENSG00000154822 2 33.3 TMEM206 ENSG00000065600 2 100 SAMD9 ENSG00000205413 2 33.3 BCL10 ENSG00000142867 2 66.7 SIK2 ENSG00000170145 2 33.3 ERMP1 ENSG00000099219 2 66.7 PHLDB2 ENSG00000144824 2 22.2 FURIN ENSG00000140564 2 66.7 HEPH ENSG00000089472 2 20

Filtered by % recurrent mutations, as defined by the 20/20 rule.

ARID2 (Supplementary Table 3). Our data, therefore, controversy about whether these two lesions should support the notion that conventional melanoma and be regarded as distinct entities.19,20 The data also do Spitzoid melanoma are genetically similar, with not support a prior assessment that Spitzoid mela- undistinguishable somatic mutation counts, typical nomas share a low-ultraviolet origin with Spitz ultraviolet signature, and the same set of driver nevi.11 We would like to note that the current study genes. These findings thus shed light on the ongoing did not assess the presence of kinase fusion in Spitz

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Figure 4 Chromatogram of PRKAR1AK92* in G762T Spitzoid melanoma (top); and IDH1R132C in 774T1 Spitz nevus and G744 Spitzoid melanoma (bottom). The amino-acid sequences adjacent to the mutation site is indicated.

Table 3 Somatic inactivating mutations (premature stop muta- found no statistical difference in mutational counts, tions, InDels, and splice-site variants) in Spitzoid melanomas and large overlap in the known melanoma driver genes. No. of % Inactivating The majority (18/21) of conventional nevi har- Gene Gene ID samples mutations bored known oncogenes, with 47.6% (10/21) BRAF ARID2 ENSG00000189079 6 66.7 and 38% (8/21) NRAS mutations. In contrast, HEPH ENSG00000089472 3 30 oncogenic HRAS p.Gly13/Gln61 mutations were AKAP9 ENSG00000127914 2 100 present in only 17.2% (5/29) of Spitz nevi, as CEP128 ENSG00000100629 2 100 described.21,22 A search for additional mutations MEN1 ENSG00000133895 2 100 7 PRKAR1A ENSG00000108946 2 100 across key melanoma drivers revealed that one Arg132Cys SLC17A5 ENSG00000119899 2 100 conventional nevus had the IDH1 mutant STAT1 ENSG00000115415 2 100 gene (Figure 4). Taken together, our data show that TAOK1 ENSG00000160551 2 100 nevi, the conventional and Spitz type, harbor few TOP2A ENSG00000131747 2 100 ASCC3 ENSG00000112249 2 66.7 ultraviolet mutations, and, with the exception of CCDC40 ENSG00000141519 2 66.7 BRAF, NRAS, and HRAS, lack additional changes in CSPG4 ENSG00000173546 2 66.7 melanoma driver genes. Our finding that at least one IPO7 ENSG00000205339 2 66.7 nevus harbored IDH1Arg132Cys suggests a transition to KIAA1328 ENSG00000150477 2 66.7 a malignant state, as recently demonstrated in a large PML ENSG00000140464 2 66.7 ATRNL1 ENSG00000107518 2 50 study showing progressive accumulation of muta- 23 CEP192 ENSG00000101639 2 50 tions in the transition to malignant melanoma. KMT2A ENSG00000118058 2 50 Approximately 55% of Spitz nevi harbor kinase TNRC6B ENSG00000100354 2 50 fusions,10 which was not assessed in the current AHNAK2 ENSG00000185567 2 28.6 study, and which likely account for the primary NF1 ENSG00000196712 2 28.6 WT TP53 ENSG00000141510 2 28.6 mutational event in the HRAS lesions. TRIOBP ENSG00000100106 2 28.6 COL4A1 ENSG00000187498 2 22.2 PLCG2 ENSG00000197943 2 22.2 Gene Mutation Burden

Filtered by % inactivating mutations, as defined by the 20/20 rule. Our data allowed us to tabulate top mutated genes in our sequenced cohort. Given our small sample size, we used the 20/20 rule as organizing principle. The nevi and Spitzoid melanomas.10 We repeated above rule identifies presumed oncogenes and tumor analyses, comparing the sequenced Spitzoid mela- suppressors by tabulating and thresholding, per nomas, all primaries, to data from primary conven- gene, the ratio of recurrent and inactivating mutational melanoma only (Supplementary Figure 1). As tions, respectively. BRAF, NRAS, and HRAS were with the full cohort of conventional melanoma, we the only recurrent oncogenic signature in

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Figure 5 Copy number alterations as shown by normalized log R ratio. These figures are plotted by Circos. (a) Spitz nevus; (b) Spitzoid melanoma; (c) conventional nevus; and (d) conventional melanoma. Blue indicates copy number losses, and red indicates copy number gains.

conventional and Spitz nevi, and there was no gene PRKAR1A cause multiple neoplasia syndrome that displayed a tumor suppressor signature Carney complex.24 The gene encodes the regulatory (Supplementary Tables 4a and b). On the other subunit type I-α of cAMP-dependent kinase, and hand, Spitzoid melanomas showed many of the same loss-of-function increases protein kinase A (PKA) top mutated genes as conventional melanomas activity leading to activated MAPK.25 Among other (Tables 2 and 3), including a recurrent signature in anomalies, Carney complex patients feature BRAF and IDH1 and inactivating signature in ARID2 intense skin pigmentation, pigmented epithelioid and TP53. Interestingly, the 20/20 rule identified melanocytomas,26 and pituitary adenomas.27 Pitui- MEN1 and PRKAR1A as potential tumor suppressors tary adenomas are also a hallmark of MEN1.27 These in Spitzoid melanomas as both genes harbored mutations occur rarely in conventional melanoma, mutations leading to early termination (Table 3 and suggesting involvement of the PKA pathway in the Figure 4). Germline inactivating mutations in genesis of Spitzoid melanomas.

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Copy Number Variant Analysis classification accuracy of 92.8% (Supplementary Figure 4, right). The decision tree for Spitzoid lesions Conventional nevi did not show any region-wide is unchanged to the one discussed above, showing a copy number alterations (Figure 5). In Spitz nevi, we 22 classification accuracy of 91.1% for the simulated observed known 11p amplifications, in addition to targeted array (Supplementary Figure 4, left). hemizygous chromosome 9 deletions (Figure 5 and Supplementary Figure 2), also described earlier.28 In contrast, Spitzoid melanomas mirrored typical copy Survival Analysis number changes observed in melanoma, including 6q, 8p, and 9p losses, as well as 6p and 8q gains We analyzed whether any of the key mutational (Figure 5). events in melanoma have an influence on patient survival. The tested mutations did not show a significant correlation with survival (Supplementary Table Comparative Genetic Analysis 6), and while some copy number alterations were associated with survival, none achieved significance Using a statistical comparison of gene-level mutation after multiple hypothesis correction. burden, we identified higher mutation rate in DNMT3A in Spitzoid melanomas compared with melanomas (Supplementary Table 5) with three MC1R Analysis predicted damaging DNMT3A mutations in Spitzoid melanoma. Recurrent DNMT3A mutations are asso- Given the PRKAR1A findings that indicated possible ciated with acute myeloid leukemia.29 Interestingly, involvement of the PKA pathway, we explored an additional mutation was observed in Spitz nevi, whether there were differences in MC1R variants supporting a possible role of DNA methylation in the among the skin lesions. Overall, there was no large pathogenesis of Spitzoid lesions. difference among the subtypes, with 44.8%, 33.3%, We further used a machine learning approach to 48.1%, and 44.4% of Spitz nevi, conventional nevi, identify key differences among the sequenced Spitzoid melanoma, and conventional melanoma lesions with regard to the identified driver muta- harboring the MC1R ‘R’ variant (see Supplementary tions, and/or chromosomal aberrations. The number Table 2). MC1R Arg151Cys and Arg160Trp were of somatic coding single-nucleotide variants (silent present most frequently across the melanoma and and non-silent) across the exome was selected as the nevi subtypes. There was a significant decrease in major distinguishing feature between the benign and Arg151Cys frequency in Spitzoid melanoma com- malignant conventional lesions. For our samples, pared with conventional melanoma (3.7% vs 22.6%, this feature allows to distinguish between conven- P = 0.0238), and a significant increase for Arg160Trp tional melanoma and nevi with an accuracy of 93.5% (25.9% vs 11.2%, P = 0.0438). (Supplementary Figure 3, right). Classification performance was 93.9% when comparing nevi with primary conventional melanoma only. For the Limitation of the Approach comparison between Spitz nevus vs Spitzoid mela- While our sequencing approach follows good prac- noma, an additional node counting the number of tices using a fully validated data analysis pipeline, known melanoma driver mutations was essential to we would like to note a couple of limitations. We achieve a classification performance of 92.9% noticed a slight increase in sequencing error rate (Supplementary Figure 3, left). The below 100% (0.4% vs 0.2%) in the FFPE samples compared with accuracy is caused by some melanomas that do not our previous melanoma data, which were largely exhibit key melanoma mutations or chromosomal derived from fresh-frozen tissue. Additionally, we aberrations. We also investigated whether data from collected two 1-mm cores from each sample for DNA small sequencing panels, rather than exome data, extraction, with adjacent normal tissue used as a achieve similar classification performance. We thus control. As a result, for a minority of collected simulated a targeted sequencing array covering 190 normal tissue, we observed slight contamination melanoma and other actionable cancer driver 30 with tumor cells, affecting somatic mutation calling. genes by filtering our exome data for mutations in Also, we needed to exclude 4% (3/80) of the initially those genes only. We assumed that chromosome sequenced samples because of inadequate DNA arm-level copy number assessment is still feasible quality. Finally, it should be noted that the discussed using the smaller panel, for example, by using classification performance is valid for the samples dedicated copy number variant sequencing 31 included in this study, which does not contain probes. As seen in Supplementary Figure 4, due ambiguous cases, such as atypical Spitz tumors. to the smaller capture region, the total number of observed somatic mutations is a less reliable decision criterion for distinguishing conventional Conclusion nevi from conventional melanoma, and the algorithm selects the number of observed copy number events We present exome-wide unbiased genomic as an additional decision node, achieving a sequencing data on Spitz nevi and conventional

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nevi, two benign lesions of the melanocytic 8 Cancer Genome Atlas Network. Genomic classification lineage, and compare the findings to Spitzoid of cutaneous melanoma. Cell 2015;161:1681–1696. and conventional melanoma. Our data suggests 9 Charbel C, Fontaine RH, Malouf GG, et al. NRAS that Spitzoid and conventional melanoma are mutation is the sole recurrent somatic mutation in large genetically similar, with possible involvement of congenital melanocytic nevi. J Invest Dermatol 2014;134:1067–1074. the PKA and DNA methylation pathways in Spitzoid 10 Wiesner T, He J, Yelensky R, et al. Kinase fusions are lesions. We also find a striking difference in the frequent in Spitz tumours and spitzoid melanomas. Nat number of somatic single-nucleotide variants, Commun 2014;5:3116. InDels, and copy number changes between the 11 Bastian BC. The molecular pathology of melanoma: an benign and malignant lesions, a finding that may integrated taxonomy of melanocytic neoplasia. 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Modern Pathology (2017) 30, 640–649